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Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Building and operationalizing data processing systems	30%	- Data ingestion and transformation 1. ETL/ELT workflows 2. Pub/Sub streaming ingestion - Data pipeline implementation 1. Dataproc and Spark-based processing 2. Dataflow pipeline development
Topic 2: Designing data processing systems	20%	- Data pipeline architecture design 1. Scalable data ingestion design 2. Batch vs streaming data processing selection - Storage and data modeling 1. Data lake architecture on Google Cloud Storage 2. Data warehouse design using BigQuery
Topic 3: Maintaining and optimizing data and ML solutions	20%	- Security and governance 1. Data encryption and compliance 2. IAM and access control - Machine learning integration 1. BigQuery ML usage 2. Vertex AI integration for pipelines
Topic 4: Operationalizing data and ML pipelines	30%	- Pipeline automation and orchestration 1. Cloud Composer workflows 2. Scheduling and monitoring pipelines - Monitoring and troubleshooting 1. Performance optimization and debugging 2. Logging and observability

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GCP-DE測試題庫，GCP-DE考試大綱

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最新的 Google Cloud Certified GCP-DE 免費考試真題 (Q41-Q46):

問題 #41
You work for a global shipping company. You want to train a model on 40 TB of data to predict which ships in each geographic region are likely to cause delivery delays on any given day. The model will be based on multiple attributes collected from multiple sources. Telemetry data, including location in GeoJSON format, will be pulled from each ship and loaded every hour. You want to have a dashboard that shows how many and which ships are likely to cause delays within a region. You want to use a storage solution that has native functionality for prediction and geospatial processing. Which storage solution should you use?

A. BigQuery
B. Cloud Datastore
C. Cloud SQL for PostgreSQL
D. Cloud Bigtable

答案：A

問題 #42
Your company produces 20,000 files every hour. Each data file is formatted as a comma separated values (CSV) file that is less than 4 KB. All files must be ingested on Google Cloud Platform before they can be processed. Your company site has a 200 ms latency to Google Cloud, and your Internet connection bandwidth is limited as 50 Mbps. You currently deploy a secure FTP (SFTP) server on a virtual machine in Google Compute Engine as the data ingestion point. A local SFTP client runs on a dedicated machine to transmit the CSV files as is. The goal is to make reports with data from the previous day available to the executives by 10:00 a.m. each day. This design is barely able to keep up with the current volume, even though the bandwidth utilization is rather low.
You are told that due to seasonality, your company expects the number of files to double for the next three months. Which two actions should you take? (choose two.)

A. Create an S3-compatible storage endpoint in your network, and use Google Cloud Storage Transfer Service to transfer on-premices data to the designated storage bucket.
B. Redesign the data ingestion process to use gsutil tool to send the CSV files to a storage bucket in parallel.
C. Contact your internet service provider (ISP) to increase your maximum bandwidth to at least 100 Mbps.
D. Assemble 1,000 files into a tape archive (TAR) fil
E. Introduce data compression for each file to increase the rate file of file transfer.
F. Transmit the TAR files instead, and disassemble the CSV files in the cloud upon receiving them.

答案：B,F

問題 #43
You are building a new application that you need to collect data from in a scalable way. Data arrives continuously from the application throughout the day, and you expect to generate approximately 150 GB of JSON data per day by the end of the year. Your requirements are: Decoupling producer from consumer Space and cost-efficient storage of the raw ingested data, which is to be stored indefinitely Near real-time SQL query Maintain at least 2 years of historical data, which will be queried with SQ Which pipeline should you use to meet these requirements?

A. Write a tool to poll the API and write data to Cloud Storage as gzipped JSON files.
B. Set up periodic exports of the database to write to Cloud Storage and load into BigQuery.
C. Create an application that provides an AP
D. Create an application that publishes events to Cloud Pub/Sub, and create Spark jobs on Cloud Dataproc to convert the JSON data to Avro format, stored on HDFS on Persistent Disk.
E. Create an application that publishes events to Cloud Pub/Sub, and create a Cloud Dataflow pipeline that transforms the JSON event payloads to Avro, writing the data to Cloud Storage and BigQuery.
F. Create an application that writes to a Cloud SQL database to store the dat

答案：C

問題 #44
You are creating a new pipeline in Google Cloud to stream IoT data from Cloud Pub/Sub through Cloud Dataflow to BigQuery. While previewing the data, you notice that roughly 2% of the data appears to be corrupt. You need to modify the Cloud Dataflow pipeline to filter out this corrupt data. What should you do?

A. Add a SideInput that returns a Boolean if the element is corrupt.
B. Add a Partition transform in Cloud Dataflow to separate valid data from corrupt data.
C. Add a GroupByKey transform in Cloud Dataflow to group all of the valid data together and discard the rest.
D. Add a ParDo transform in Cloud Dataflow to discard corrupt elements.

答案：D

問題 #45
As your organization expands its usage of GCP, many teams have started to create their own projects. Projects are further multiplied to accommodate different stages of deployments and target audiences. Each project requires unique access control configurations. The central IT team needs to have access to all projects. Furthermore, data from Cloud Storage buckets and BigQuery datasets must be shared for use in other projects in an ad hoc way. You want to simplify access control management by minimizing the number of policies. Which two steps should you take? Choose 2 answers.

A. Find all the active members who have access to these projects, and create a Cloud IAM policy to grant access to all these users.
B. Use Cloud Deployment Manager to automate access provision.
C. For each Cloud Storage bucket or BigQuery dataset, decide which projects need acces
D. Introduce resource hierarchy to leverage access control policy inheritance.
E. Create distinct groups for various teams, and specify groups in Cloud IAM policies.
F. Only use service accounts when sharing data for Cloud Storage buckets and BigQuery datasets.

答案：D,E

問題 #46
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